In this work, we propose a novel approach to simultaneous localization and mapping (SLAM) able to generate such semantic maps by using three-dimensional laser range scans.
Our approach exploits ideas from a modern LiDAR SLAM pipeline [1]} and incorporates semantic information obtained from a semantic segmentation generated by a Fully Convolutional Neural Network (FCN) [2]}.
This allows us to generate high-quality semantic maps, while at the same time improve the geometry of the map and the quality of the odometry.
